The Global Positioning System (GPS) is a satellite navigation system, or satellite positioning system, designed to provide position, velocity and time information almost anywhere in the world. GPS was developed by the Unites States Department of Defense, and currently includes a constellation of twenty-four operational satellites. Other types of satellite navigation systems include the Wide Area Augmentation System (WAAS), the Global Navigation Satellite System (GLONASS) deployed by the Russian Federation, and the Galileo system planned by the European Union. As used herein, “satellite positioning system”(SPS) will be understood to refer to GPS, Galileo, GLONASS, NAVSTAR, GNSS, a system that uses satellites from a combination of these systems, pseudolite systems, or any SPS developed in the future.
A variety of receivers have been designed to decode the signals transmitted from the satellites to determine position, velocity or time. In general, to decipher the signals and compute a final position, the receiver must acquire signals from the satellites in view, measure and track the received signals, and recover navigational data from the signals. By accurately measuring the distance from three different satellites, the receiver triangulates its position, i.e., solves for a latitude, longitude and altitude. In particular, the receiver measures distance by measuring the time required for each signal to travel from the respective satellite to the receiver. This requires precise time information. For this reason, measurements from a fourth satellite are typically required to help resolve common time common measurement errors, e.g., errors created by the inaccuracies of timing circuits within the receiver.
In certain locations, e.g., urban environments with tall buildings, the receiver may only be able to acquire signals from three or less satellites. In these situations, the receiver will be unable to resolve all four variables of the position solution: latitude, longitude, altitude, and time. If the receiver is able to acquire signals from three satellites, for example, the receiver may forego an altitude calculation to resolve latitude, longitude and time. Alternately, if altitude is obtained via external means, all four variables may be resolved from three satellite signals. If less than three signals are available, the receiver may be unable to calculate its position.
To address this limitation, many receivers employ hybrid location technology that makes use of signals from base stations of a wireless communication system. As with satellite signals, the hybrid receivers measure time delays of the wireless signals to measure distances from the base stations of the network. The hybrid receivers utilize the signals from the base stations, as well as any acquired signals from GPS satellites, to resolve the position and time variables. The hybrid location technique often allows a receiver to compute a position solution in a wide variety of locations where conventional positioning techniques would fail. In code division multiple access (CDMA) mobile wireless systems, for example, this base station measurement portion of this hybrid technique is referred to as Advanced Forward Link Trilateration (AFLT).
The accuracy of the location solution determined by the receiver is affected by the degree of time precision within the system. In synchronized systems, such as existing CDMA systems, the timing information communicated by the cellular base stations is synchronized with the timing information from the GPS satellites, allowing precise time to be available throughout the system. In some systems, such as the Global System for Mobile Communications (GSM), the timing information is not synchronized between the base stations and the GPS satellites. In these systems, Location Measurement Units (LMUs) are added to the existing infrastructure to provide precise timing information for the wireless network.
Another technique that is commonly used in position determining systems and algorithms is the use of Kalman filters. As is well known, a Kalman filter (KF) is an optimal recursive data estimation algorithm. It is frequently used to model attributes of moving entities such as aircraft, people, vehicles etc. These attributes can include both velocity and position, for example. The current state of the system and a current measurement are used to estimate a new state of the system. In practice, a Kalman filter combines all available measurement data, plus prior knowledge about the system, measuring devices, and error statistics to produce an estimate of the desired variables in such a manner that the error is minimized statistically.
In the past, a Kalman filter used within a mobile telecommunications device typically required certain initialization parameters from an accompanying position system receiver. For example, when a GPS receiver was used, it was typical that simultaneous measurements from at least three different satellite vehicles were obtained before the Kalman filter could be initialized. This means that in one measurement epoch, signals from at least three different satellite vehicles are received and successfully processed by the mobile communications device. This requirement degrades performance of the mobile device because it may take on the order of tens of seconds to acquire signals from three satellite vehicles, especially in urban environments. If the necessary signals are not acquired or are not acquired in a timely manner, then the position determining portion of the mobile device may fail to initialize and may not operate properly or efficiently.
Thus, the typical initialization of a Kalman filter used for position determination of a mobile unit requires that the complete initial state at some time t0 be obtained first before updated position state information can be estimated for times t>t0. This restriction implies that for mobile GPS receivers in marginal signal environments, for example, with time varying obstructions to the line of sight to the satellites, it may difficult or time consuming to acquire simultaneous (i.e., within the same epoch) range measurements from at least 3 GPS satellites needed for Kalman filter initialization. It is highly desirable to improve position determination performance for mobile GPS receivers in harsh signal environments where simultaneity of range measurements may not occur in a timely fashion.
Accordingly, a need remains to improve the position determining capabilities of mobile communications devices and to do so in a timely and efficient way.